Donald 1.1.2

Donald — A fast and minimal task manager for Asyncio.

Donald supports both synchronous and asynchronous functions. It can run coroutines across multiple event loops, schedule periodic tasks, and consume jobs from AMQP queues.

Contents

• Key Features
• Requirements
• Installation
• Quick Start
• Task Tuning
• Scheduling Tasks
• Running in Production
• Bug tracker
• Contributing
• License

Key Features

• Simple and lightweight API
• Works with asyncio (Python 3.9+)
• Supports multiple backends: memory, redis, amqp
• Periodic task scheduling (cron or intervals)
• Built-in retry mechanism and failbacks
• Can run multiple workers and schedulers in separate processes

Requirements

• Python 3.9 or newer

Installation

Install via pip:

pip install donald

With Redis backend support:

pip install donald[redis]

Quick Start

Initialize a task manager:

import logging
from donald import Donald

# Init Donald
manager = Donald(

    # Params (default values)
    # -----------------------

    # Setup logging
    log_level=logging.INFO,
    log_config=None,

    # Choose a backend (memory|redis|amqp)
    # memory - is only recommended for testing/local development
    backend='memory',

    # Backend connection params
    # redis: {'url': 'redis://localhost:6379/0', 'channel': 'donald'}
    # amqp: {'url': 'amqp://guest:guest@localhost:5672/', 'queue': 'donald', 'exchange': 'donald'}
    backend_params={},

    # Tasks worker params
    worker_params={
      # Max tasks in work
      'max_tasks': 0,

      # Tasks default params (delay, timeout)
      'task_defaults': {},

      # A awaitable function to run on worker start
      'on_start': None

      # A awaitable function to run on worker stop
      'on_stop': None

      # A awaitable function to run on worker error
      'on_error': None

    },
)

# Wrap a function to task
@manager.task()
async def mytask(*args, **kwargs):
    # Do some job here

# Start the manager somewhere (on app start for example)
await manager.start()

# you may run a worker in the same process
# not recommended for production
worker = manager.create_worker()
worker.start()

# ...

# Submit the task to workers
mytask.submit(*args, **kwargs)

# ...

# Stop the manager when you need
await worker.stop()
await manager.stop()

Task Tuning

# Set delay and timeout
@tasks.task(delay=5, timeout=60)
async def delayed_task(*args, **kwargs):
    ...

# Automatic retries on error
@tasks.task(retries_max=3, retries_backoff_factor=2, retries_backoff_max=60)
async def retrying_task(*args, **kwargs):
    ...

# Define a failback function
@retrying_task.failback()
async def on_fail(*args, **kwargs):
    ...

# Manual retry control
@tasks.task(bind=True)
async def conditional_retry(self):
    try:
        ...
    except Exception:
        if self.retries < 3:
            self.retry()
        else:
            raise

Scheduling Tasks

@tasks.task()
async def mytask(*args, **kwargs):
    ...

# Run every 5 minutes
mytask.schedule('*/5 * * * *')

# Start the scheduler (not recommended in production)
manager.scheduler.start()

# Stop it when needed
manager.scheduler.stop()

Running in Production

Create a task manager in tasks.py:

from donald import Donald

manager = Donald(backend='amqp')

# Define your tasks and schedules

Start a worker in a separate process:

$ donald -M tasks.manager worker

Start the scheduler (optional):

$ donald -M tasks.manager scheduler

Bug tracker

Found a bug or have a feature request? Please open an issue: 👉 https://github.com/klen/donald/issues

Contributing

Contributions are welcome! Development happens on GitHub: 🔗 https://github.com/klen/donald

License

Licensed under a MIT license.